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Machine learning can sniff out tell-tale signs of shady URLs so you don’t get phished.

The problem: The internet is riddled with websites set up for the sole purpose of stealing a user’s information or installing malware on a victim’s machine. Antivirus companies blacklist them as fast as they can, but with new sites launched every day, it’s a Sisyphean effort to keep up.

AI to the rescue: A new system called URLNet uses neural networks that look at character-level and word-level combinations in—you guessed it—the site’s URL to detect a site’s risk. URLs contain clues to whether a site is malicious, like length and misspelled domain names.

Results: The researchers trained URLNet on two data sets, one containing a million legit and malicious URLs and one with five million. In each case, URLNet beat other current systems at detecting suspicious sites.

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Jackie SnowI am MIT Technology Review’s associate editor for artificial intelligence. I cover stories about where AI is currently, where it’s headed, and what’s wrong with the hype around the technology. I also put together The Algorithm, our daily newsletter on the latest in artificial intelligence. Previously I worked for Fast Company and have been published by the New York Times, National Geographic, Wall Street Journal, and others.

ImageJackie Snow

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Author

Jackie SnowI am MIT Technology Review’s associate editor for artificial intelligence. I cover stories about where AI is currently, where it’s headed, and what’s wrong with the hype around the technology. I also put together The Algorithm, our daily newsletter on the latest in artificial intelligence. Previously I worked for Fast Company and have been published by the New York Times, National Geographic, Wall Street Journal, and others.

ImageJackie Snow

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